Failure mode and effect analysis Submitted by- Ankita rajvi samra nasir Shikha maurya Shreya solanki

Failure Mode and Effect Analysis Failure Modes and Effects Analysis (FMEA) is a systematic, proactive method for evaluating a process to identify where and how it might fail and to assess the relative impact of different failures, in order to identify the parts of the process that are most in need of change. FMEA is particularly useful in evaluating a new process prior to implementation and in assessing the impact of a proposed change to an existing process.

A structured approach to: Identifying the ways in which a product or process can fail Estimating risk associated with specific causes Prioritizing the actions that should be taken to reduce risk Evaluating design validation plan (design FMEA) or current control plan (process FMEA)

When to conduct FMEA When a process, product or service is being designed or redesigned, after quality function deployment. When an existing process, product or service is being applied in a new way. Before developing control plans for a new or modified process. When improvement goals are planned for an existing process, product or service. When analyzing failures of an existing process, product or service. Periodically throughout the life of the process, product or service After system, product, or process functions are defined, but before specific hardware is selected or released to manufacturing.

History of FMEA First used in the 1960’s in the Aerospace industry during the Apollo missions In 1974, the Navy developed MIL-STD-1629 regarding the use of FMEA In the late 1970’s, the automotive industry was driven by liability costs to use FMEA Later, the automotive industry saw the advantages of using this tool to reduce risks related to poor quality

The FMEA Form Identify failure modes and their effects Identify causes of the failure modes and controls Prioritize Determine and assess actions

Types of FMEA Design Analyzes product design before release to production, with a focus on product function Analyzes systems and subsystems in early concept and design stages Process Used to analyze manufacturing and assembly processes after they are implemented

FMEA: A Team Tool(team input required) A team approach is necessary. Team should be led by the Process Owner who is the responsible manufacturing engineer or technical person, or other similar individual familiar with FMEA. The following should be considered for team members: – Design Engineers – Operators – Process Engineers – Reliability – Materials Suppliers – Suppliers – Customers

FMEA Procedure 1. For each process input (start with high value inputs), determine the ways in which the input can go wrong (failure mode) 2. For each failure mode, determine effects Select a severity level for each effect 3. Identify potential causes of each failure mode Select an occurrence level for each cause 4. List current controls for each cause Select a detection level for each cause

Calculate the Risk Priority Number (RPN) 6. Develop recommended actions, assign responsible persons, and take actions Give priority to high RPNs MUST look at severities rated a 10 7. Assign the predicted severity, occurrence, and detection levels and compare RPNs

FMEA Inputs and Outputs(information flow) C&E Matrix Process Map Process History Procedures Knowledge Experience List of actions to prevent causes or detect failure modes History of actions taken FMEA

Analyzing Failure & Effects Severity, Occurrence and Detection Severity Importance of the effect on customer requirements Occurrence Frequency with which a given cause occurs and creates failure modes (obtain from past data if possible) Detection The ability of the current control scheme to detect (then prevent) a given cause (may be difficult to estimate early in process operations).

Assigning Rating Weights Rating Scales There are a wide variety of scoring “anchors”, both quantitative or qualitative Two types of scales are 1-5 or 1-10 The 1-5 scale makes it easier for the teams to decide on scores The 1-10 scale may allow for better precision in estimates and a wide variation in scores (most common)

Rating scales Severity 1 = Not Severe, 10 = Very Severe Occurrence 1 = Not Likely, 10 = Very Likely Detection 1 = Easy to Detect, 10 = Not easy to Detect

Calculating a Composite Score Risk Priority Number – RPN ) RPN is the product of the severity, occurrence, and detection scores. RPN Severity X Occurrence X Detection =

Benefits Allows us to identify areas of our process that most impact our customers Helps us identify how our process is most likely to fail Points to process failures that are most difficult to detect

Application Examples Manufacturing: A manager is responsible for moving a manufacturing operation to a new facility. He/she wants to be sure the move goes as smoothly as possible and that there are no surprises. Design: A design engineer wants to think of all the possible ways a product being designed could fail so that robustness can be built into the product. Software: A software engineer wants to think of possible problems a software product could fail when scaled up to large databases. This is a core issue for the Internet.

CASE STUDY

Introduction Yarn is used in the manufacture of seat belt used in a vehicle. Yarn consists of many filaments. Broken filament was a major defect commonly found in producing the yarn. A sheet of yarn was considered as defective when there were two or more broken filaments per sheet. In the case study factory, the defective rate from broken filament defects was 3.35%, which was 62.4% of the total defects. There were many possible factors, which can cause the broken filament defect. These factors were mainly machine-related. Thus, there was a need to identify the major root causes and solve them to obtain the lower defective rate.

LITERATURE REVIEW The mechanical properties such as the stretch of the yarn, the tension of the yarn, and the friction between the yarn and the machine could cause the broken filament defect. The tension of the polyester yarn was related to the setting of machine factors such as the spinning temperature, the velocity at the spinneret outlet, the temperature of quench air, the velocity of quench air, and the spinning velocity.

Furthermore, the parameters influencing the quality of the modern fibre draw process were the fibre radius ,the draw speed, and the draw tension. Moreover, the parameters in the spinning and the drawing processes also affected the quality of the yarn.

There were many factors that could cause the broken filament defect There were many factors that could cause the broken filament defect. However, solving all the causes was not cost-effective. Thus, there is a need to prioritize the causes and improve on important factors. Thus, this research has the aim to investigate all possible factors in the Direct Spin Draw process and prioritize those causes to obtain important factors to be improved.

First the FMEA team was set up. Then, the team applied the concept of the criteria of FMEA to prioritize the causes to be improved. These criteria are the severity rating of the failure mode effects, the occurrence rating of failure causes, and the detection rating of the effectiveness of the detection methods

The description of the severity rating was not developed since in this case, there is only one type of defect under consideration. The severity ratings of all failure modes are the same, which is 9 (filaments breakage) Next, the team used the risk priority number (RPN), which is the multiplication of the severity rating, the occurrence rating, and the detection rating to prioritize the failure causes. The causes with higher RPNs were considered as the more important causes, which would be selected and solved further.

The description of the developed occurrence rating and detecting rating

After defining the criteria for FMEA analysis, then the team brainstormed to list out all potential failure modes and causes, Then, the score ratings were given and RPN was calculated. 8causes were improved by changing the detection method or determining more appropriate detection frequency. The causes related to parameter setting were suggested to be studied further to help find the optimal setting.

FMEA Analysis and improvement solutions Refer excel sheet